A comparative study of low multidirectional locked nailing and locking compression plating in management of distal tibia fractures

Background: Ideal management for distal tibial meta-diaphyseal fracture remains controversial, due to lack of adequate evidences about implants and multiple treatment modalities. Most commonly these fractures were dealt with either locking compression plate (LCP) or by multi directional intra-medullary nail (IMIL). Aim is to compare these two implants, to understand the mechanism and find out the ideal implant for the management of distal tibia fractures.Methods: This study is prospective and comparative done at the associated hospitals of KMC Mangalore, spanning a period of around 2 years (October 2014 to July 2016). All patients presented with distal tibial meta-diaphyseal fractures were included in the study. Patients were treated with either low multi directional IMIL nail or by LCP and followed up for a minimum period of 6 months. Outcome measures included Olerud Molander Ankle Score (OMAS), wound issues, union of the fracture and patient mobility.Results: 50 consecutive patients (mean age 40 years) were included in the study, divided equally into 2 groups. Group-A treated with multi directional IMIL nail and group-B with MIPPO plating. Mean functional OMAS score for nailing is 91 and for plating is 88. All fractures treated with nailing united within 6 months and 4% patient treated by plating goes mal union and 4% infected.Conclusions: For distal tibial fracture management, intra-medullary nailing proved reliable surgical option with regards to the OMAS score, fracture union and less infection rates

A novel AI-enabled framework to diagnose Coronavirus COVID-19 using smartphone embedded sensors: design study

This is an accepted manuscript of an article published by IEEE (in press). The accepted version of the publication may differ from the final published version.Coronaviruses are a famous family of viruses that cause illness in both humans and animals. The new type of coronavirus COVID-19 was firstly discovered in Wuhan, China. However, recently, the virus has widely spread in most of the world and causing a pandemic according to the World Health Organization (WHO). Further, nowadays, all the world countries are striving to control the COVID-19. There are many mechanisms to detect coronavirus including clinical analysis of chest CT scan images and blood test results. The confirmed COVID-19 patient manifests as fever, tiredness, and dry cough. Particularly, several techniques can be used to detect the initial results of the virus such as medical detection Kits. However, such devices are incurring huge cost, taking time to instal them and use. Therefore, in this paper, a new framework is proposed to detect COVID-19 using built-in smartphone sensors. The proposal provides a low-cost solution, since most of radiologists have already held smartphones for different daily purposes. Not only that but also ordinary people can use the framework on their smartphones for the virus detection purposes. Today’s smartphones are powerful with existing computationrich processors, memory space, and large number of sensors including cameras, microphone, temperature sensor, inertial sensors, proximity, colour-sensor, humidity-sensor, and wireless chipsets/sensors. The designed Artificial Intelligence (AI) enabled framework reads the smartphone sensors’ signal measurements to predict the grade of severity of the pneumonia as well as predicting the result of the disease

Enabling accurate indoor localization for different platforms for smart cities using a transfer learning algorithm

This is an accepted manuscript of an article published by Wiley in Internet Technology Letters on 17/09/2020, available online: https://doi.org/10.1002/itl2.200 The accepted version of the publication may differ from the final published version.Indoor localization algorithms in smart cities often use Wi‐Fi fingerprints as a database of Received Signal Strength (RSS) and its corresponding position coordinate for position estimation. However, the issue of fingerprinting is the use of different platform‐devices. To this end, we propose a Long Short‐Term Memory (LSTM)‐based novel indoor positioning mechanism in smart city environment. We used LSTM, a type of recurrent neural network to process sequential data of users’ trajectory in indoor buildings. The proposed approach first utilizes a database of normalizing fingerprint landmarks to calculateWiFi Access Points (WAPs) RSS values to mitigate the fluctuation issue and then apply the normalization parameters on the RSS values during the online phase. Afterwards, we constructed a transfer model to adapt the RSS values during the offline phase and then applying it on the RSS values from the different smartphones during the online phase. Thorough simulation results confirm that the proposed approach can obtain 1.5 to 2 meters positioning accuracy for indoor environments, which is 60 % higher than traditional approaches

Systemic manifestations of primary Sjögren's syndrome out of the ESSDAI classification: prevalence and clinical relevance in a large international, multi-ethnic cohort of patients

OBJECTIVES: To analyse the frequency and characterise the systemic presentation of primary Sjögren’s syndrome (SS) out of the ESSDAI classification in a large international, multi-ethnic cohort of patients. // METHODS: The Big Data Sjögren Project Consortium is an international, multicentre registry based on world-wide data-sharing and cooperative merging of pre-existing clinical SS databases from leading centres in clinical research in SS from the five continents. A list of 26 organ-by-organ systemic features not currently included in the ESSDAI classification was defined according to previous studies; these features were retrospectively recorded. // RESULTS: Information about non-ESSDAI features was available in 6331 patients [5,917 female, mean age at diagnosis 52 years, mainly White (86.3%)]. A total of 1641 (26%) patients had at least one of the ESSDAI systemic features. Cardiovascular manifestations were the most frequent organ-specific group of non-ESSDAI features reported in our patients (17% of the total cohort), with Raynaud’s phenomenon being reported in 15%. Patients with systemic disease due to non-ESSDAI features had a lower frequency of dry mouth (90.7% vs. 94.1%, p<0.001) and positive minor salivary gland biopsy (86.7% vs. 89%, p=0.033), a higher frequency of anti-Ro/SSA (74.7% vs. 68.7%, p<0.001), anti-La/SSB antibodies (44.5% vs. 40.4%, p=0.004), ANA (82.7% vs. 79.5%, p=0.006), low C3 levels (17.4% vs. 9.7%, p<0.001), low C4 levels (14.4% vs. 9.6%, p<0.001), and positive serum cryoglobulins (8.6% vs. 5.5%, p=0.001). Systemic activity measured by the ESSDAI, clinESSDAI and DAS was higher in patients with systemic disease out of the ESSDAI in comparison with those without these features (p<0.001 for all comparisons). // CONCLUSIONS: More than a quarter of patients with primary SS may have systemic manifestations not currently included in the ESSDAI classification, with a wide variety of cardiovascular, digestive, pulmonary, neurological, ocular, ENT (ear, nose, and throat), cutaneous and urological features that increase the scope of the systemic phenotype of the disease. However, the individual frequency of each of these non-ESSDAI features was very low, except for Raynaud’s phenomenon

Epidemiological profile and north-south gradient driving baseline systemic involvement of primary Sjögren's syndrome

OBJECTIVE To characterize the systemic phenotype of primary Sjögren’s syndrome at diagnosis by analysing the EULAR-SS disease activity index (ESSDAI) scores. METHODS The Sjögren Big Data Consortium is an international, multicentre registry based on worldwide data-sharing cooperative merging of pre-existing databases from leading centres in clinical research in Sjögren’s syndrome from the five continents. RESULTS The cohort included 10 007 patients (9352 female, mean 53 years) with recorded ESSDAI scores available. At diagnosis, the mean total ESSDAI score was 6.1; 81.8% of patients had systemic activity (ESSDAI score ≥1). Males had a higher mean ESSDAI (8.1 vs 6.0, P 65 years, P < 0.001). The highest global ESSDAI score was reported in Black/African Americans, followed by White, Asian and Hispanic patients (6.7, 6.5, 5.4 and 4.8, respectively; P < 0.001). The frequency of involvement of each systemic organ also differed between ethnic groups, with Black/African American patients showing the highest frequencies in the lymphadenopathy, articular, peripheral nervous system, CNS and biological domains, White patients in the glandular, cutaneous and muscular domains, Asian patients in the pulmonary, renal and haematological domains and Hispanic patients in the constitutional domain. Systemic activity measured by the ESSDAI, clinical ESSDAI (clinESSDAI) and disease activity states was higher in patients from southern countries (P < 0.001). CONCLUSION The systemic phenotype of primary Sjögren’s syndrome is strongly influenced by personal determinants such as age, gender, ethnicity and place of residence, which are key geoepidemiological players in driving the expression of systemic disease at diagnosis